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Found 9 papers, 1 papers with code
Collaborative learning of common latent representations in routinely collected multivariate ICU physiological signals
In Intensive Care Units (ICU), the abundance of multivariate time series presents an opportunity for machine learning (ML) to enhance patient phenotyping.
Core consistency diagnosis for Block Term Decomposition in rank $(L_r, L_r, 1)$
Our results confirm that CORCONDIA can be extended to BTD $(L_r, L_r, 1)$, and the resulting metric can assist in the process of determining the number of components in this tensor factorisation.
VAE-IF: Deep feature extraction with averaging for unsupervised artifact detection in routine acquired ICU time-series
Our approach offers a promising solution for cleaning ICU data in clinical research and practice without the need for any labels whatsoever.
Graph-Based Permutation Patterns for the Analysis of Task-Related fMRI Signals on DTI Networks in Mild Cognitive Impairment
Permutation Entropy ($PE$) is a powerful nonlinear analysis technique for univariate time series.
Hybrid Artifact Detection System for Minute Resolution Blood Pressure Signals from ICU
Manual annotation by experienced researchers, which is the gold standard for removing artifacts, is time-consuming and costly due to the volume of the data generated in the ICU.
An Efficient Mixture of Deep and Machine Learning Models for COVID-19 and Tuberculosis Detection Using X-Ray Images in Resource Limited Settings
The pipeline was computationally efficient requiring just 0. 19 second to extract DF per X-ray image and 2 minutes for training a traditional classifier with more than 2000 images on a CPU machine.
On the use of higher-order tensors to model muscle synergies
Here, we introduce a 4th-order tensor muscle synergy model that extends the current state of the art by taking spectral information and repetitions (movements) into account.
Consistency of Muscle Synergies Extracted via Higher-Order Tensor Decomposition Towards Myoelectric Control
In recent years, muscle synergies have been pro-posed for proportional myoelectric control.
Higher order tensor decomposition for proportional myoelectric control based on muscle synergies
In sum, this study provides the first proof of concept for the use of higher-order tensor decomposition in proportional myoelectric control and it highlights the potential of tensors to provide an objective and direct approach to identify synergies.
